A new use of UWB radar to detecting victims and discriminating humans from animals

Yu, Xiao; Jiao, Teng; Lv, Hao; Zhang, Y.; Li, Z.; Wang, J. Q.

doi:10.1109/icgpr.2016.7572661

Cited by 8 publications

(11 citation statements)

References 10 publications

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“…The correlation between signals along the slow-time index around the target position of a human target is much more intense than that of a dog target [16,33]. Thus, two correlation-coefficient-category features are extracted to reflect the difference from the aspect of correlation of signals at different range points.…”

Section: Two Correlation-coefficient-category Featuresmentioning

confidence: 99%

A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar

Wang

Huang

et al. 2021

EURASIP J. Adv. Signal Process.

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Pets have been indispensable members for many families in modern life, especially significant for the elderly and the blind. However, they may cause false alarm when misused as signal source in non-contact monitoring of the vital signs using ultra-wideband (UWB) radar. Distinguishing between humans and pets can help ensure the correct signal source. Nevertheless, existing solutions are few or only utilize a single feature, which can hinder robustness and accuracy because of individual differences. In this study, we proposed a robust multi-feature based method to solve the problem. First, 19 discriminative features were extracted to reflect differences in aspects of energy, frequency, wavelet entropy, and correlation coefficient. Second, the features were ranked by recursive feature elimination algorithm and the top eight were then selected to build an optimal support vector machine (SVM) model. The area under the receiver operating characteristic curve (AUC) of the optimal SVM model reached 0.9620. The false and missing alarms for identifying humans were 0.0962 and 0.0600, respectively. Finally, comparison with the state-of-the-art method that only employed one feature validated the advance and accuracy of the proposed method. The method is envisioned to facilitate the UWB radar applications in non-contact and continuous vital signs monitoring.

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Section: Two Correlation-coefficient-category Featuresmentioning

confidence: 99%

A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar

Wang

Huang

et al. 2021

EURASIP J. Adv. Signal Process.

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“…However, under a complex rubble environment, there exists the possibility that some animals are also present, and since the vital signs of humans and animals are similar, misjudgment may occur. To solve this problem, we have proposed some novel methods to distinguish between humans and animals [23][24][25]. During search and rescue missions, the survival condition of a victim is an essential piece of information for the diagnosis and treatment of injury.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Determination of Vital Signs Monitoring of Animals in Hemorrhage States Using Bio-Radar

Yu¹,

Yin²,

Lv³

et al. 2021

PIER M

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With the rapid development and in-depth research of non-contact bio-radar-based detection technology, researchers have recently been putting more emphasis on target identification. Living status identification, a hotspot of target identification research, is particularly useful in search and rescue missions. During such missions, in order to rescue victims and provide corresponding medical support in a timely manner, it is necessary to acquire the survival information of victims, especially when they are injured. Hence, the vital signs extracted from a radar signal should be considered as the crucial parameters to reflect the living status. To determine living status through analyzing vital signs, this study utilized a bio-radar system to continuously monitor hemorrhagic animals, which simulated injured persons with hemorrhagic symptoms. Moreover, we defined and classified three survival periods based on changes in vital signs combined with a K-nearest neighbor algorithm (KNN) classifier. Experimental results show that we can approximately determine the current living status of animals with this method, which can aid in providing information for on-site rescue and follow-up medical treatment.

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“…However, for vital sign monitoring radars with high resolution and high SNR, the difference in wavelet entropy between humans and dogs is not evident. Moreover, a correlation coefficient-based method has also been proposed to distinguish between humans and dogs [26]. The correlation coefficient in [26] shows the strength and direction of the linear relationship between the target variables and others.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, a correlation coefficient-based method has also been proposed to distinguish between humans and dogs [26]. The correlation coefficient in [26] shows the strength and direction of the linear relationship between the target variables and others. However, similar to [25], the correlation coefficient is directly related to the noise level of the radar system.…”

Section: Introductionmentioning

confidence: 99%

Method for Distinguishing Humans and Animals in Vital Signs Monitoring Using IR-UWB Radar

Wang

Zhang

et al. 2019

IJERPH

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Radar has been widely applied in many scenarios as a critical remote sensing tool for non-contact vital sign monitoring, particularly for sleep monitoring and heart rate measurement within the home environment. For non-contact monitoring with radar, interference from house pets is an important issue that has been neglected in the past. Many animals have respiratory frequencies similar to those of humans, and they are easily mistaken for human targets in non-contact monitoring, which would trigger a false alarm because of incorrect physiological parameters from the animal. In this study, humans and common pets in families, such as dogs, cats, and rabbits, were detected using an impulse radio ultrawideband (IR-UWB) radar, and the echo signals were analyzed in the time and frequency domains. Subsequently, based on the distinct in-body structure between humans and animals, we propose a parameter, the respiratory and heartbeat energy ratio (RHER), which reflects the contribution rate of breathing and heartbeat in the detected vital signs. Combining this parameter with the energy index, we developed a novel scheme to distinguish between humans and animals. In the developed scheme, after background noise removal and direct-current component suppression, an energy indicator is used to initially identify the target. The signal is then decomposed using a variational mode decomposition algorithm, and the variational intrinsic mode functions that represent human respiration and heartbeat components are obtained and utilized to calculate the RHER parameter. Finally, the RHER index is applied to rapidly distinguish between humans and animals. Our experimental results demonstrate that the proposed approach more effectively distinguishes between humans and animals in terms of monitoring vital signs than the existing methods. Furthermore, its rapidity and need for only minimal calculation resources enable it to meet the needs of real-time monitoring.

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A new use of UWB radar to detecting victims and discriminating humans from animals

Cited by 8 publications

References 10 publications

A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar

A robust multi-feature based method for distinguishing between humans and pets to ensure signal source in vital signs monitoring using UWB radar

Non-Contact Determination of Vital Signs Monitoring of Animals in Hemorrhage States Using Bio-Radar

Method for Distinguishing Humans and Animals in Vital Signs Monitoring Using IR-UWB Radar

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